NQA

Author:

Wang Xiaokang1,Yang Laurence T.1ORCID,Li Hongguo2,Lin Man3,Han Jianjun2,Apduhan Bernady O.4

Affiliation:

1. University of Electronic Science and Technology of China; St. Francis Xavier University, Antigonish, NS, Canada

2. Huazhong University of Science and Technology, Wuhan, China

3. St. Francis Xavier University, Antigonish, NS, Canada

4. Kyushu Sangyo University, Fukuoka, Japan

Abstract

Radio frequency identification (RFID) systems, as one of the key components in the Internet of Things (IoT), have attracted much attention in the domains of industry and academia. In practice, the performance of RFID systems rather relies on the effectiveness and efficiency of anti-collision algorithms. A large body of studies have recently focused on the anti-collision algorithms, such as the Q-algorithm ( QA ), which has been successfully utilized in EPCglobal Class-1 Generation-2 protocol. However, the performance of those anti-collision algorithms needs to be further improved. Observe that fully exploiting the pre-processing time can improve the efficiency of the QA algorithm. With an objective of improving the performance for anti-collision, we propose a Nested Q-algorithm ( NQA ), which makes full use of such pre-processing time and incorporates the advantages of both Binary Tree ( BT ) algorithm and QA algorithm. Specifically, based on the expected number of collision tags, the NQA algorithm can adaptively select either BT or QA to identify collision tags. Extensive simulation results validate the efficiency and effectiveness of our proposed NQA (i.e., less running time for processing the same number of active tags) when compared to the existing algorithms.

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture,Software

Reference28 articles.

Cited by 66 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimization of dynamic frame slot ALOHA algorithm based on back propagation neural network;Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023);2023-10-09

2. RFID tag group recognition based on motion blur estimation and YOLOv2 improved by Gaussian;Metrology and Measurement Systems;2023-07-26

3. A Robust and Efficient Anti-Collision Technique for RFID Readers in the Mobile Environments based Machine Learning;2022 Sixth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC);2022-11-10

4. A Generalized Low-Rank Double-Tensor Nuclear Norm Completion Framework for Infrared Small Target Detection;IEEE Transactions on Aerospace and Electronic Systems;2022-08

5. When Infrared Small Target Detection Meets Tensor Ring Decomposition: A Multiscale Morphological Framework;IEEE Transactions on Aerospace and Electronic Systems;2022-08

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